Acer BW under nutrients and disease
Ana M. Palacio
2021-07-06
General project set-up
# Libraries
library(reshape2)
library(tidyverse)
# Graphs
theme_set (theme_classic() + theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
axis.line = element_line(colour = "black"),
legend.position="none",
axis.text.x = element_text(angle = 90, vjust = 0.5),
plot.title = element_text(size=12, face="bold"),
#panel.border = element_rect(colour = "black", fill=NA, size=1)
panel.border = element_blank()
))
# Data
# Fragment list
#CurrentFragments<-read.csv("8.Metadata/Frag_assignments.csv", header = T)
# Collection information
#Location<-read.csv("8.Metadata/Genotypes.csv", header = T)Meta data
- Treatments, Genotypes, Tags
# Treatment Info
Treatment<-read.csv("Data/Treatments.csv")
#duplicated(Treatment$Tag)
Treatment<-Treatment[!duplicated(Treatment$Tag), ]
#duplicated(Treatment$Tag)
summary(Treatment)## Tag Genotype Nutrients Disease Available
## 201 : 1 Elkhorn : 29 Ambient:129 Dead : 1 Dead : 10
## 202 : 1 Kelsey-1: 26 Dead : 1 Extra : 6 Experiment:246
## 204 : 1 U44 : 26 Extra : 6 Pathogen:136 Extra? : 7
## 205 : 1 Acerv2 : 25 NH4 :126 Placebo :119
## 207 : 1 FM19 : 25 TL : 1 TL : 1
## 208 : 1 FM6 : 25
## (Other):257 (Other) :107
## A_Tank Blast_Tank
## Min. :1.000 Min. :1.00
## 1st Qu.:3.000 1st Qu.:2.00
## Median :4.500 Median :4.00
## Mean :4.504 Mean :4.37
## 3rd Qu.:6.000 3rd Qu.:6.00
## Max. :8.000 Max. :8.00
## NA's :17 NA's :17# Tags weight
Tags<-read.csv("Data/Tags_W2.csv")
#duplicated(Tags$Tag)
Tags<-Tags[!duplicated(Tags$Tag), ]
Tags<-Tags %>% select(T_Type, Tag, T_AW, T_BW, Tag_Vol.cm3.)
#duplicated(Tags$Tag)
summary(Tags)## T_Type Tag T_AW T_BW Tag_Vol.cm3.
## B:200 201 : 1 Min. :10.01 Min. :1.393 Min. : 8.401
## S: 77 202 : 1 1st Qu.:10.14 1st Qu.:1.443 1st Qu.: 8.524
## 204 : 1 Median :16.26 Median :2.285 Median :13.672
## 205 : 1 Mean :14.56 Mean :2.050 Mean :12.250
## 207 : 1 3rd Qu.:16.31 3rd Qu.:2.298 3rd Qu.:13.726
## 208 : 1 Max. :16.48 Max. :2.333 Max. :13.865
## (Other):271 # Tags$T_SW_den<-(999.842594+0.06793952*(Tags$Temperature)-0.00909529*(Tags$Temperature)^2+0.0001001685* (Tags$Temperature)^3-0.000001120083*(Tags$Temperature)^4+0.000000006536332*(Tags$Temperature)^5+(0.824493-0.0040899*(Tags$Temperature)+0.000076438*(Tags$Temperature)^2-0.00000082467*(Tags$Temperature)^3+0.0000000053875* (Tags$Temperature)^4)*(Tags$Salinity)+(-0.00572466+0.00010227*(Tags$Temperature)-0.0000016546*(Tags$Temperature)^2)* (Tags$Salinity)^1.5+0.00048314*(Tags$Salinity)^2)*0.001
#Tags$Estimated_Density<-(-Tags$SW_den/((Tags$T_BW/Tags$T_AW)-1))
Tags %>%
group_by(T_Type) %>%
summarise_at(vars(T_AW, T_BW, Tag_Vol.cm3.), funs(mean(., na.rm=TRUE)))BW data
# 1. BW data
BW_Tall<-read.csv("Data/BW_ Long_data.csv")
#BW_Tall<-read.csv("Data/BW_ Long_data2.csv")
#BW_Tall$Estimated.W.density[BW_Tall$Estimated.W.density == "#VALUE!" ] <-NA
#BW_Tall$Estimated.W.density<-as.numeric(as.character(BW_Tall$Estimated.W.density))
#BW_Tall$Estimated_AW [BW_Tall$Estimated_AW == "#VALUE!" ] <-NA
#BW_Tall$Estimated_AW<-as.numeric(as.character(BW_Tall$Estimated_AW))
# 2. Data clean-up an types:
# Variable types
#BW_Tall$Time <- as.factor(BW_Tall$Time)
#BW_Tall$Time<-as.numeric(BW_Tall$Time)
BW_Tall$Date<-as.Date(BW_Tall$Date, "%Y-%m-%d")
BW_Tall$Day<-(as.numeric(BW_Tall$Date)-18486)
# Remove-unused data
#Extras <- BW_Tall[which (BW_Tall$Nutrients=="Extra"), ]
#BW_Tall <- droplevels(BW_Tall[!rownames(BW_Tall) %in%
# rownames(Extras), ])
# 3. Merge with treatments
BW_Tall<-plyr::join(BW_Tall, Treatment, by = "Tag",
type = "left", match = "all")
BW_Tall<-plyr::join(BW_Tall, Tags, by = "Tag",
type = "left", match = "all")
BW_Tall$Nutrients<-factor(BW_Tall$Nutrients,
levels= c("Ambient", "NH4"), ordered=TRUE)
BW_Tall$Disease<-factor(BW_Tall$Disease,
levels= c("Placebo", "Pathogen"), ordered=TRUE)
# 4. Replicates
BW_Tall$Tank<-factor(BW_Tall$Tank, ordered=FALSE)
BW_Tall$Genotype<-factor(BW_Tall$Genotype, ordered=FALSE)
summary(BW_Tall)## Tag Tank Date BW
## 201 : 10 4 :285 Min. :2020-08-12 Min. : 2.515
## 202 : 10 3 :282 1st Qu.:2020-09-04 1st Qu.: 4.009
## 204 : 10 2 :280 Median :2020-09-18 Median : 4.793
## 205 : 10 6 :279 Mean :2020-09-18 Mean : 5.320
## 207 : 10 1 :276 3rd Qu.:2020-10-09 3rd Qu.: 6.353
## 208 : 10 7 :276 Max. :2020-10-15 Max. :11.950
## (Other):2431 (Other):813 NA's :3 NA's :4
## Temp Sal
## Min. :26.10 Min. :31.5
## 1st Qu.:27.00 1st Qu.:32.2
## Median :27.40 Median :33.2
## Mean :27.35 Mean :33.1
## 3rd Qu.:27.70 3rd Qu.:34.0
## Max. :29.10 Max. :34.7
## NA's :3 NA's :3
## Notes
## :2414
## new to the experiment : 6
## New : 5
## water sample 3 : 3
## Broke in 3 pieces 10-14-2020 : 2
## BW from 10-09 probably wrong. I think it is supposed to be 8.3398: 2
## (Other) : 59
## Day Genotype Nutrients Disease
## Min. : 0.00 FM19 : 247 Ambient:1235 Placebo :1155
## 1st Qu.:23.00 Kelsey-1: 242 NH4 :1241 Pathogen:1321
## Median :37.00 U44 : 242 NA's : 15 NA's : 15
## Mean :37.74 FM6 : 241
## 3rd Qu.:58.00 Acerv2 : 240
## Max. :64.00 (Other) :1276
## NA's :3 NA's : 3
## Available A_Tank Blast_Tank T_Type T_AW
## Dead : 39 Min. :1.000 Min. :1.000 B :1760 Min. :10.01
## Experiment:2438 1st Qu.:3.000 1st Qu.:2.000 S : 728 1st Qu.:10.14
## Extra? : 11 Median :5.000 Median :4.000 NA's: 3 Median :16.25
## NA's : 3 Mean :4.516 Mean :4.373 Mean :14.47
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:16.31
## Max. :8.000 Max. :8.000 Max. :16.48
## NA's :53 NA's :53 NA's :3
## T_BW Tag_Vol.cm3.
## Min. :1.393 Min. : 8.401
## 1st Qu.:1.440 1st Qu.: 8.519
## Median :2.284 Median :13.666
## Mean :2.037 Mean :12.172
## 3rd Qu.:2.298 3rd Qu.:13.725
## Max. :2.333 Max. :13.865
## NA's :3 NA's :3Water density BW tag corrections
# 1. Water density
BW_Tall$SW_den<-(999.842594+0.06793952*(BW_Tall$Temp)-0.00909529*(BW_Tall$Temp)^2+0.0001001685*
(BW_Tall$Temp)^3-0.000001120083*(BW_Tall$Temp)^4+0.000000006536332*(BW_Tall$Temp)^5+(0.824493-0.0040899*
(BW_Tall$Temp)+0.000076438*(BW_Tall$Temp)^2-0.00000082467*(BW_Tall$Temp)^3+0.0000000053875*(BW_Tall$Temp)^4)*
(BW_Tall$Sal)+(-0.00572466+0.00010227*(BW_Tall$Temp)-0.0000016546*(BW_Tall$Temp)^2)*
(BW_Tall$Sal)^1.5+0.00048314*(BW_Tall$Sal)^2)*0.001
# 2. Tag_temperature_salinity_specific BW
BW_Tall$T_BW_S <-BW_Tall$T_AW-(BW_Tall$SW_den * BW_Tall$Tag_Vol.cm3.)
# 3. Coral BW
BW_Tall$Coral_BW<-((BW_Tall$BW)-(BW_Tall$T_BW_S))BW calculations
BW.data<-BW_Tall[order(BW_Tall$Tag), ]
# 4. Calculate days bw BW data points
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(Days = Day - lag(Day, default = Day[1]))
# 5. Calculate change in BW bw data points
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dBW = Coral_BW - lag(Coral_BW, default = Coral_BW[1]))
BW.data$dBW[which(BW.data$Tag=="A129"& BW.data$Date=="2020-10-02")]<-NA
BW.data$dBW[which(BW.data$Tag=="A017"& BW.data$Date=="2020-09-18")]<-NA
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dBW_r = dBW/(Days*lag(Coral_BW, default = Coral_BW[1])))
BW.data$dBW_r<-BW.data$dBW_r*1000
summary(BW.data)## Tag Tank Date BW
## 201 : 10 4 :285 Min. :2020-08-12 Min. : 2.515
## 202 : 10 3 :282 1st Qu.:2020-09-04 1st Qu.: 4.009
## 204 : 10 2 :280 Median :2020-09-18 Median : 4.793
## 205 : 10 6 :279 Mean :2020-09-18 Mean : 5.320
## 207 : 10 1 :276 3rd Qu.:2020-10-09 3rd Qu.: 6.353
## 208 : 10 7 :276 Max. :2020-10-15 Max. :11.950
## (Other):2431 (Other):813 NA's :3 NA's :4
## Temp Sal
## Min. :26.10 Min. :31.5
## 1st Qu.:27.00 1st Qu.:32.2
## Median :27.40 Median :33.2
## Mean :27.35 Mean :33.1
## 3rd Qu.:27.70 3rd Qu.:34.0
## Max. :29.10 Max. :34.7
## NA's :3 NA's :3
## Notes
## :2414
## new to the experiment : 6
## New : 5
## water sample 3 : 3
## Broke in 3 pieces 10-14-2020 : 2
## BW from 10-09 probably wrong. I think it is supposed to be 8.3398: 2
## (Other) : 59
## Day Genotype Nutrients Disease
## Min. : 0.00 FM19 : 247 Ambient:1235 Placebo :1155
## 1st Qu.:23.00 Kelsey-1: 242 NH4 :1241 Pathogen:1321
## Median :37.00 U44 : 242 NA's : 15 NA's : 15
## Mean :37.74 FM6 : 241
## 3rd Qu.:58.00 Acerv2 : 240
## Max. :64.00 (Other) :1276
## NA's :3 NA's : 3
## Available A_Tank Blast_Tank T_Type T_AW
## Dead : 39 Min. :1.000 Min. :1.000 B :1760 Min. :10.01
## Experiment:2438 1st Qu.:3.000 1st Qu.:2.000 S : 728 1st Qu.:10.14
## Extra? : 11 Median :5.000 Median :4.000 NA's: 3 Median :16.25
## NA's : 3 Mean :4.516 Mean :4.373 Mean :14.47
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:16.31
## Max. :8.000 Max. :8.000 Max. :16.48
## NA's :53 NA's :53 NA's :3
## T_BW Tag_Vol.cm3. SW_den T_BW_S
## Min. :1.393 Min. : 8.401 Min. :1.020 Min. :1.387
## 1st Qu.:1.440 1st Qu.: 8.519 1st Qu.:1.021 1st Qu.:1.446
## Median :2.284 Median :13.666 Median :1.021 Median :2.286
## Mean :2.037 Mean :12.172 Mean :1.021 Mean :2.043
## 3rd Qu.:2.298 3rd Qu.:13.725 3rd Qu.:1.022 3rd Qu.:2.306
## Max. :2.333 Max. :13.865 Max. :1.022 Max. :2.360
## NA's :3 NA's :3 NA's :3 NA's :3
## Coral_BW Days dBW dBW_r
## Min. : 0.408 Min. : 0.000 Min. :-0.043590 Min. :-6.716
## 1st Qu.: 1.764 1st Qu.: 6.000 1st Qu.: 0.000552 1st Qu.: 1.003
## Median : 2.687 Median : 7.000 Median : 0.028408 Median : 1.908
## Mean : 3.278 Mean : 6.381 Mean : 0.041328 Mean : 2.106
## 3rd Qu.: 4.247 3rd Qu.: 7.000 3rd Qu.: 0.057556 3rd Qu.: 3.059
## Max. :10.523 Max. :14.000 Max. : 0.350371 Max. :12.416
## NA's :4 NA's :3 NA's :7 NA's :515 BW.data<-BW.data[which(BW.data$Available!="Dead"),]
BW.data<-BW.data[which(BW.data$Available!="Extra?"),]AW calculations analysis
# 6. Coral air weight
BW.data$Est_Coral_AW<-(BW.data$Coral_BW)*(1/(1-(BW.data$SW_den)/2.4))
# 7. Calculate change in AW bw data points
BW.data<-BW.data %>%
dplyr::group_by(Tag) %>%
dplyr::mutate(dAW = Est_Coral_AW - lag(Est_Coral_AW, default = Est_Coral_AW[1]))
BW.data$dAW[which(BW.data$Tag=="A129"& BW.data$Date=="2020-10-02")]<-NA
BW.data$dAW[which(BW.data$Tag=="A017"& BW.data$Date=="2020-09-18")]<-NA
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dAW_d = dAW/Days)
BW.data<-BW.data %>%
group_by(Tag) %>%
dplyr::mutate(dAW_r = dAW/(Days*lag(Est_Coral_AW, default = Est_Coral_AW[1])))
BW.data$dAW_r<-BW.data$dAW_r*1000
summary(BW.data)## Tag Tank Date BW
## 201 : 10 4 :285 Min. :2020-08-12 Min. : 2.515
## 202 : 10 2 :277 1st Qu.:2020-09-04 1st Qu.: 4.019
## 204 : 10 1 :274 Median :2020-09-25 Median : 4.810
## 205 : 10 5 :274 Mean :2020-09-19 Mean : 5.330
## 207 : 10 6 :274 3rd Qu.:2020-10-09 3rd Qu.: 6.360
## 208 : 10 3 :270 Max. :2020-10-15 Max. :11.888
## (Other):2378 (Other):784 NA's :1
## Temp Sal
## Min. :26.10 Min. :31.50
## 1st Qu.:27.00 1st Qu.:32.20
## Median :27.40 Median :33.20
## Mean :27.34 Mean :33.09
## 3rd Qu.:27.70 3rd Qu.:34.00
## Max. :29.10 Max. :34.70
##
## Notes
## :2368
## new to the experiment : 4
## water sample 3 : 3
## Broke in 3 pieces 10-14-2020 : 2
## BW from 10-09 probably wrong. I think it is supposed to be 8.3398: 2
## pale : 2
## (Other) : 57
## Day Genotype Nutrients Disease
## Min. : 0.00 FM19 :247 Ambient:1211 Placebo :1137
## 1st Qu.:23.00 FM14 :240 NH4 :1227 Pathogen:1301
## Median :44.00 FM6 :240
## Mean :38.17 U44 :240
## 3rd Qu.:58.00 Kelsey-1:239
## Max. :64.00 Acerv2 :237
## (Other) :995
## Available A_Tank Blast_Tank T_Type T_AW
## Dead : 0 Min. :1.000 Min. :1.000 B:1717 Min. :10.01
## Experiment:2438 1st Qu.:3.000 1st Qu.:2.000 S: 721 1st Qu.:10.14
## Extra? : 0 Median :5.000 Median :4.000 Median :16.25
## Mean :4.516 Mean :4.373 Mean :14.45
## 3rd Qu.:6.000 3rd Qu.:6.000 3rd Qu.:16.31
## Max. :8.000 Max. :8.000 Max. :16.48
##
## T_BW Tag_Vol.cm3. SW_den T_BW_S
## Min. :1.393 Min. : 8.401 Min. :1.020 Min. :1.387
## 1st Qu.:1.440 1st Qu.: 8.519 1st Qu.:1.021 1st Qu.:1.445
## Median :2.284 Median :13.665 Median :1.021 Median :2.287
## Mean :2.034 Mean :12.156 Mean :1.021 Mean :2.040
## 3rd Qu.:2.298 3rd Qu.:13.725 3rd Qu.:1.022 3rd Qu.:2.306
## Max. :2.333 Max. :13.865 Max. :1.022 Max. :2.360
##
## Coral_BW Days dBW dBW_r
## Min. : 0.408 Min. : 0.000 Min. :-0.04359 Min. :-6.716
## 1st Qu.: 1.786 1st Qu.: 6.000 1st Qu.: 0.00122 1st Qu.: 0.986
## Median : 2.706 Median : 7.000 Median : 0.02833 Median : 1.882
## Mean : 3.290 Mean : 6.379 Mean : 0.04109 Mean : 2.078
## 3rd Qu.: 4.270 3rd Qu.: 7.000 3rd Qu.: 0.05699 3rd Qu.: 3.010
## Max. :10.446 Max. :14.000 Max. : 0.35037 Max. :12.416
## NA's :1 NA's :4 NA's :495
## Est_Coral_AW dAW dAW_d dAW_r
## Min. : 0.7097 Min. :-0.077175 Min. :-0.0110 Min. :-6.7871
## 1st Qu.: 3.1083 1st Qu.: 0.003817 1st Qu.: 0.0049 1st Qu.: 0.9518
## Median : 4.7084 Median : 0.048208 Median : 0.0086 Median : 1.8469
## Mean : 5.7274 Mean : 0.071708 Mean : 0.0103 Mean : 2.0802
## 3rd Qu.: 7.4270 3rd Qu.: 0.099988 3rd Qu.: 0.0140 3rd Qu.: 3.0176
## Max. :18.1770 Max. : 0.610562 Max. : 0.0556 Max. :12.5749
## NA's :1 NA's :4 NA's :495 NA's :495#write.csv(BW.data, "BW.data.csv", row.names = FALSE)
BW.CRF<-BW.data[which(BW.data$Genotype=='K2'|
BW.data$Genotype=='U41'|
BW.data$Genotype=='U44'), ]
BW.UM<-BW.data[which(BW.data$Genotype=='Acerv2'|
BW.data$Genotype=='Cooper-9'|
BW.data$Genotype=='Elkhorn'|
BW.data$Genotype=='Kelsey-1'), ]
BW.FWC<-BW.data[which(BW.data$Genotype=='FM14'|
BW.data$Genotype=='FM6'|
BW.data$Genotype=='FM19'|
BW.data$Genotype=='FM9'), ]BW Exploration
SizeBiasCheck<- ggplot(BW.data, aes (BW, dBW_r, colour=factor(Date))) +
#geom_smooth(method = "lm")+
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 6,2),
expand = c(0, 0),
name=("rBW (mg / g*day)"))
SizeBiasCheck
SizeBiasCheck<- ggplot(BW.data, aes (BW, dBW_r, colour=factor(Genotype))) +
#geom_smooth(method = "lm")+
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 15),
breaks = seq(0, 15,2),
expand = c(0, 0),
name=("rBW (mg / g*day)"))
SizeBiasCheck
Genotype
BW_Genet<- ggplot(BW.data, aes (Date, Coral_BW, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 12),
breaks = seq(0, 15,1),
expand = c(0, 0),
name=("BW [g]"))+
theme(legend.position = "bottom")
BW_Genet
BW_Genet+facet_grid(~Nutrients)
BW_Genet+facet_grid(Disease~Nutrients)
dBW_Genet<- ggplot(BW.data, aes (Date, dBW, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=1) +
#scale_y_continuous(limits = c(-1.4, 1.5),
# breaks = seq(-1.4, 1.5, 0.4),
# expand = c(0, 0),
# name=("dBW [g]"))+
theme(legend.position = "bottom")
dBW_Genet
dBW_Genet+ facet_wrap(~Nutrients)
dBW_Genet + facet_wrap(Nutrients~Disease)
dBWr_Genet<- ggplot(BW.data, aes (Date, dBW_r, colour=factor(Genotype))) +
geom_jitter(alpha=0.3) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
#scale_y_continuous(limits = c(-2, 6),
# breaks = seq(-2, 10, 1),
# expand = c(0, 0),
# name=("dBW [mg / g*day]"))+
theme(legend.position = "bottom")
dBWr_Genet
dBWr_Genet+ facet_wrap(~Nutrients)
dBWr_Genet + facet_wrap(Nutrients~Disease)
Pre-treatment bias
BW_Bias<- ggplot(BW.data, aes (Genotype, dBW_r, colour=factor(Nutrients))) +
geom_jitter(alpha=0.3) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 5,1),
expand = c(0, 0),
name=("dBW [mg / g*day]"))
BW_Bias+facet_grid(~Date)
AW Exploration
SizeBiasCheck<- ggplot(BW.data, aes (Est_Coral_AW, dAW_r, colour=factor(Genotype))) +
#stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
#stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("dAW [mg / g*day]"))
ggExtra::ggMarginal(
p = SizeBiasCheck,
type = 'density',
margins = 'both',
size = 5,
colour = 'black',
fill = 'gray'
)
Genotype
AW_Genet<- ggplot(BW.data, aes (Date, Est_Coral_AW, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 20),
breaks = seq(0, 20,1),
expand = c(0, 0),
name=("estimated AW (g)"))+
theme(legend.position = "bottom")
AW_Genet
AW_Genet+facet_grid(~Nutrients)
AW_Genet+facet_grid(Disease~Nutrients)
dAW_Genet<- ggplot(BW.data, aes (Date, dAW, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
theme(legend.position = "none")+
geom_jitter(alpha=0.5) + theme(legend.position = "bottom")+
scale_y_continuous(limits = c(-0.18, 0.7),
breaks = seq(-1, 1,0.1),
expand = c(0, 0),
name=("dAW (g)"))
dAW_Genet
dAW_Genet+ facet_wrap(~Nutrients)
dAW_Genet+ facet_wrap(~Disease)
dAW_Gd<- ggplot(BW.data, aes (Date, dAW_d*1000, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
theme(legend.position = "none")+
geom_jitter(alpha=0.5) + theme(legend.position = "bottom")+
scale_y_continuous(limits = c(-15, 60),
breaks = seq(-15, 60, 5),
expand = c(0, 0),
name=("dAW (mg/d)"))
dAW_Gd
dAW_Gd+ facet_wrap(~Nutrients)
dAW_Gd+ facet_wrap(~Disease)
dAWr_Genet<- ggplot(BW.data, aes (Date, dAW_r, colour=factor(Genotype))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
theme(legend.position = "none")+
geom_jitter(alpha=0.5) + theme(legend.position = "bottom")+
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 8,2),
expand = c(0, 0),
name=("AW (mg / g*day)"))
dAWr_Genet
dAWr_Genet+ facet_wrap(~Nutrients)
dAWr_Genet+ facet_wrap(~Disease)
dAW_Genet2<- ggplot(BW.data, aes (Date, dAW, colour=Genotype)) +
geom_vline(xintercept = as.Date("2020-08-12"), linetype=3)+
geom_vline(xintercept = as.Date("2020-08-21"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-04"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-11"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-14"), linetype=4)+
geom_vline(xintercept = as.Date("2020-09-18"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-25"), linetype=3)+
geom_vline(xintercept = as.Date("2020-10-02"), linetype=3)+
geom_vline(xintercept = as.Date("2020-10-09"), linetype=3)+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_vline(xintercept = as.Date("2020-09-14"), linetype=3)+
theme(legend.position = "none")+
geom_jitter(shape=21, alpha=0.3)+
scale_y_continuous(limits = c(-.2, 0.5),
breaks = seq(0, 1,0.05),
expand = c(0, 0),
name=("Growth (mg / g d)"))+
scale_x_date(limits = c(as.Date("2020-08-20"), as.Date("2020-10-20")),
breaks = "7 day",
expand = c(0, 0),
name=("Growth (mg / g d)"))+
theme(legend.position = "bottom")
dAW_Genet2
dAW_Genet2 + facet_wrap(~Nutrients)
dAW_Genet2 + facet_wrap(~Tank)
dAWr_Genet2<- ggplot(BW.data, aes (Date, dAW_r, colour=Genotype)) +
geom_vline(xintercept = as.Date("2020-08-12"), linetype=3)+
geom_vline(xintercept = as.Date("2020-08-21"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-04"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-11"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-14"), linetype=4)+
geom_vline(xintercept = as.Date("2020-09-18"), linetype=3)+
geom_vline(xintercept = as.Date("2020-09-25"), linetype=3)+
geom_vline(xintercept = as.Date("2020-10-02"), linetype=3)+
geom_vline(xintercept = as.Date("2020-10-09"), linetype=3)+
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_vline(xintercept = as.Date("2020-09-14"), linetype=3)+
theme(legend.position = "none")+
#geom_jitter(shape=21, alpha=0.3)+
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 10,0.5),
expand = c(0, 0),
name=("Growth (mg / g d)"))+
scale_x_date(limits = c(as.Date("2020-08-20"), as.Date("2020-10-20")),
breaks = "7 day",
expand = c(0, 0),
name=("Growth (mg / g d)"))+
theme(legend.position = "bottom")
dAWr_Genet2
dAWr_Genet2 + facet_wrap(~Nutrients)
dAWr_Genet2 + facet_wrap(~Tank)
BW_Frag<- ggplot(BW.data, aes (Date, dAW_r, group=(Tag),
colour=factor(Genotype))) +
geom_line()+
geom_hline(yintercept = 0, linetype=3)+
#scale_x_continuous(name="Days",
# breaks = seq(0, 30, by=15)) +
scale_y_continuous(name="Growth (mg / g d)",
limits = c(-2, 10),
breaks = seq(-1, 10, by=1)) +
theme(legend.position="bottom",
legend.title = element_blank(),
strip.background =element_rect(fill=NA))
BW_Frag+facet_wrap(Nutrients~Genotype)
Tank
AW_Tank<- ggplot(BW.data, aes (Tank, dAW_r)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(0, 10),
breaks = seq(0, 10,2),
expand = c(0, 0),
name=("Growth rate (mg / g day)"))+
geom_jitter(aes(colour=Genotype), shape=21, alpha=0.3)+
facet_wrap(~Date) +
theme(legend.position = "bottom")
AW_Tank
#AW_Tank+ facet_wrap(Date~Nutrients)
#AW_Tank+ facet_wrap(Date~Disease)
AW_Disease<- ggplot(BW.data, aes (Date, dAW_r, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-3, 10),
breaks = seq(-3, 10,2),
expand = c(0, 0),
name=("Growth rate (mg/g day)"))+
geom_jitter(alpha=0.3)+
theme(legend.position = "bottom")
AW_Disease
Treatments
AW<- ggplot(BW.data, aes (Date, Est_Coral_AW, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-3, 10),
breaks = seq(-3, 10,2),
expand = c(0, 0),
name=("Estimated AW (g)"))+
geom_jitter(alpha=0.3, shape=21)+
theme(legend.position = "bottom")
AW
AW + facet_grid(Disease~Nutrients)
D_AW<- ggplot(BW.data, aes (Date, dAW, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-2, 2),
breaks = seq(-2, 2, 0.2),
expand = c(0, 0),
name=("Growth (g)"))+
geom_jitter(alpha=0.3)+
theme(legend.position = "bottom")
D_AW
D_AW + facet_wrap(~Nutrients)
D_AW + facet_wrap(Nutrients~Disease)
D_AW<- ggplot(BW.data, aes (Date, dAW_r, colour=Genotype)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-3, 10),
breaks = seq(-3, 10,2),
expand = c(0, 0),
name=("Growth rate (mg/g day)"))+
geom_jitter(alpha=0.3, shape=21)+
theme(legend.position = "bottom")
D_AW
D_AW + facet_wrap(~Nutrients)
D_AW + facet_wrap(~Disease)
Fragments
AW_Disease<- ggplot(BW.data, aes (Date, dAW_r, group=Tag,
colour=Nutrients)) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
scale_y_continuous(limits = c(-3, 10),
breaks = seq(-3, 10,2),
expand = c(0, 0),
name=("Growth rate (mg/g day)"))+
geom_jitter(alpha=0.3)+
theme(legend.position = "bottom")
AW_Disease
AW_Disease + facet_wrap(~Tank)
AW_Disease + facet_wrap(Nutrients~Disease)
Pre-treatment bias
AW_Bias<- ggplot(BW.data, aes (Date, dAW_r, colour=factor(Genotype), shape=(Nutrients))) +
stat_summary(fun.data = "mean_cl_boot",geom = "errorbar", width = 0.2 )+
stat_summary(fun.y=mean, geom="line") +
geom_jitter(alpha=0.5) +
scale_y_continuous(limits = c(0, 5),
breaks = seq(0, 5,1),
expand = c(0, 0),
name=("dBW [mg / g*day]"))
AW_Bias + facet_grid(~Disease)+
theme(legend.position = "bottom")
#Summary<-BW.data %>% group_by(Genotype, Nutrients, Disease) %>% count(Date)
#BW_Tall2<-BW.data %>% group_by(Disease, Genotype, Nutrients) %>% add_count(Date)
#write.csv(Summary, "numbers.csv")Packages used
# Creates bibliography
#knitr::write_bib(c(.packages()), "packages.bib")Henry, Lionel, and Hadley Wickham. 2019. Purrr: Functional Programming Tools. https://CRAN.R-project.org/package=purrr.
Müller, Kirill, and Hadley Wickham. 2019. Tibble: Simple Data Frames. https://CRAN.R-project.org/package=tibble.
R Core Team. 2020. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/.
Wickham, Hadley. 2017a. Reshape2: Flexibly Reshape Data: A Reboot of the Reshape Package. https://CRAN.R-project.org/package=reshape2.
———. 2017b. Tidyverse: Easily Install and Load the ’Tidyverse’. https://CRAN.R-project.org/package=tidyverse.
———. 2019a. Forcats: Tools for Working with Categorical Variables (Factors). https://CRAN.R-project.org/package=forcats.
———. 2019b. Stringr: Simple, Consistent Wrappers for Common String Operations. https://CRAN.R-project.org/package=stringr.
Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, and Hiroaki Yutani. 2019. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://CRAN.R-project.org/package=ggplot2.
Wickham, Hadley, Romain François, Lionel Henry, and Kirill Müller. 2019. Dplyr: A Grammar of Data Manipulation. https://CRAN.R-project.org/package=dplyr.
Wickham, Hadley, and Lionel Henry. 2020. Tidyr: Tidy Messy Data. https://CRAN.R-project.org/package=tidyr.
Wickham, Hadley, Jim Hester, and Romain Francois. 2018. Readr: Read Rectangular Text Data. https://CRAN.R-project.org/package=readr.